{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Tce3stUlHN0L"
      },
      "source": [
        "##### Copyright 2025 Google LLC."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "cellView": "form",
        "id": "tuOe1ymfHZPu"
      },
      "outputs": [],
      "source": [
        "# @title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "# you may not use this file except in compliance with the License.\n",
        "# You may obtain a copy of the License at\n",
        "#\n",
        "# https://www.apache.org/licenses/LICENSE-2.0\n",
        "#\n",
        "# Unless required by applicable law or agreed to in writing, software\n",
        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "# See the License for the specific language governing permissions and\n",
        "# limitations under the License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dfsDR_omdNea"
      },
      "source": [
        "# Gemini API: Read a PDF\n",
        "\n",
        "This notebook demonstrates how you can convert a PDF file so that it can be read by the Gemini API.\n",
        "\n",
        "<a target=\"_blank\" href=\"https://colab.research.google.com/github/google-gemini/cookbook/blob/main/quickstarts/PDF_Files.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" height=30/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FaqZItBdeokU"
      },
      "source": [
        "## Setup"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XKJ78ne3O0sB"
      },
      "outputs": [],
      "source": [
        "%pip install -Uq 'google-genai>=1.0.0'"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2dteVdL4Y_t9"
      },
      "source": [
        "## Configure your API key\n",
        "\n",
        "To run the following cell, your API key must be stored in a Colab Secret named `GOOGLE_API_KEY`. If you don't already have an API key, or you're not sure how to create a Colab Secret, see [Authentication](https://github.com/google-gemini/cookbook/blob/main/quickstarts/Authentication.ipynb) for an example."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "A9sUQ4WrP-Yr"
      },
      "outputs": [],
      "source": [
        "from google import genai\n",
        "from google.colab import userdata\n",
        "\n",
        "GOOGLE_API_KEY = userdata.get(\"GOOGLE_API_KEY\")\n",
        "client = genai.Client(api_key=GOOGLE_API_KEY)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jZj7pRt7exwE"
      },
      "source": [
        "## Download and inspect the PDF"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "thYL8XGjerMa"
      },
      "source": [
        "Install the PDF processing tools. You don't need these to use the API, it's just used to display a screenshot of a page."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "iK30_utL1DhY"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree... Done\n",
            "Reading state information... Done\n",
            "The following NEW packages will be installed:\n",
            "  poppler-utils\n",
            "0 upgraded, 1 newly installed, 0 to remove and 35 not upgraded.\n",
            "Need to get 186 kB of archives.\n",
            "After this operation, 697 kB of additional disk space will be used.\n",
            "Get:1 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 poppler-utils amd64 22.02.0-2ubuntu0.8 [186 kB]\n",
            "Fetched 186 kB in 0s (722 kB/s)\n",
            "Selecting previously unselected package poppler-utils.\n",
            "(Reading database ... 126111 files and directories currently installed.)\n",
            "Preparing to unpack .../poppler-utils_22.02.0-2ubuntu0.8_amd64.deb ...\n",
            "Unpacking poppler-utils (22.02.0-2ubuntu0.8) ...\n",
            "Setting up poppler-utils (22.02.0-2ubuntu0.8) ...\n",
            "Processing triggers for man-db (2.10.2-1) ...\n"
          ]
        }
      ],
      "source": [
        "!apt install poppler-utils"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WibRLdf2_Qoq"
      },
      "source": [
        "This PDF page is an article titled [Smoothly editing material properties of objects with text-to-image models and synthetic data](https://research.google/blog/smoothly-editing-material-properties-of-objects-with-text-to-image-models-and-synthetic-data/) available on the Google Research Blog."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "1k8Vy_1w1z6G"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
            "\r\n",
            "  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\r\n",
            "100 6538k  100 6538k    0     0  29.6M      0 --:--:-- --:--:-- --:--:-- 29.6M\n"
          ]
        }
      ],
      "source": [
        "import pathlib\n",
        "\n",
        "if not pathlib.Path('test.pdf').exists():\n",
        "  !curl -o test.pdf https://storage.googleapis.com/generativeai-downloads/data/Smoothly%20editing%20material%20properties%20of%20objects%20with%20text-to-image%20models%20and%20synthetic%20data.pdf"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hmIj4eQlfFot"
      },
      "source": [
        "Look at one of the pages:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "fH4WmrY_1MdQ"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "page-image-1.jpg  sample_data  test.pdf\n"
          ]
        }
      ],
      "source": [
        "!pdftoppm test.pdf -f 1 -l 1 page-image -jpeg\n",
        "!ls"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "9b0MfUwc17Mk"
      },
      "outputs": [
        {
          "data": {
            "image/jpeg": "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",
            "text/plain": [
              "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=618x800>"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import PIL.Image\n",
        "\n",
        "img = PIL.Image.open(f\"page-image-1.jpg\")\n",
        "img.thumbnail([800, 800])\n",
        "img"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OhmqFY7o4eHM"
      },
      "source": [
        "## Upload the file to the API\n",
        "\n",
        "Start by uploading the PDF using the [File API](./File_API.ipynb)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "xgSJtXA427hF"
      },
      "outputs": [],
      "source": [
        "file_ref = client.files.upload(file='test.pdf')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yXFZFUJHgTcU"
      },
      "source": [
        "## Try it out\n",
        "\n",
        "Now select the model you want to use in this guide, either by selecting one in the list or writing it down. Keep in mind that some models, like the 2.5 ones are thinking models and thus take slightly more time to respond (cf. [thinking notebook](./Get_started_thinking.ipynb) for more details and in particular learn how to switch the thiking off).\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "c7f7ebc3dde9"
      },
      "outputs": [],
      "source": [
        "MODEL_ID = \"gemini-2.5-flash\" # @param [\"gemini-2.5-flash-lite\", \"gemini-2.5-flash\", \"gemini-2.5-pro\",\"gemini-3-pro-preview\"] {\"allow-input\":true, isTemplate: true}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HcFASSbqlTt5"
      },
      "source": [
        "The pages of the PDF file are each passed to the model as a screenshot of the page plus the text extracted by OCR."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "be7VSZ7Q3UAR"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "1560"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "client.models.count_tokens(\n",
        "    model=MODEL_ID,\n",
        "    contents=[file_ref, 'Can you summarize this file as a bulleted list?']\n",
        ").total_tokens"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "123016f7809e"
      },
      "outputs": [],
      "source": [
        "response = client.models.generate_content(\n",
        "    model=MODEL_ID,\n",
        "    contents=[file_ref, 'Can you summarize this file as a bulleted list?']\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "700bb45acbc8"
      },
      "outputs": [
        {
          "data": {
            "text/markdown": [
              "Here's a summary of the document as a bulleted list:\n",
              "\n",
              "*   **Problem Addressed:** The document introduces a new method to overcome the challenges of smoothly and photorealistically editing material properties (like color, shininess, or transparency) of objects in existing images, a task difficult for traditional tools and existing text-to-image (T2I) models which often struggle to disentangle material from shape.\n",
              "*   **Proposed Solution:** Augmenting T2I models with parametric editing capabilities, leveraging synthetic data for fine-tuning. This work is detailed in their paper \"Alchemist: Parametric Control of Material Properties with Diffusion Models\" (CVPR 2024).\n",
              "*   **Methodology:**\n",
              "    *   A novel synthetic dataset was created using physically-based rendering of 100 3D household objects. For each \"base image,\" a single material attribute (e.g., roughness, transparency) was varied with defined \"edit strengths\" (-1 for minimum change, +1 for maximum) while keeping object shape, lighting, and camera angle constant.\n",
              "    *   A latent diffusion model (specifically, Stable Diffusion 1.5) was fine-tuned on this synthetic dataset. The model inputs include a context image, a text instruction (e.g., \"change the metallic of the pot\"), and a scalar edit strength.\n",
              "    *   The model learns to edit only the specified material property and generalizes well from the synthetic data to real-world images.\n",
              "*   **Results & Performance:**\n",
              "    *   The method successfully changes object appearance (e.g., metallic, transparent) while preserving the object's shape and image lighting, realistically filling in backgrounds for transparent objects and showing caustic effects.\n",
              "    *   A user study found their method produced more photorealistic edits (69.6% vs. 30.4%) and was strongly preferred (70.2% vs. 29.8%) compared to a baseline method (InstructPix2Pix).\n",
              "*   **Applications:**\n",
              "    *   Enables practical use cases like visualizing room changes, and rapid prototyping for architects, artists, and designers mocking up new product designs.\n",
              "    *   The edits are visually consistent, allowing for integration with downstream 3D tasks, such as editing input images and then training a new NeRF (Neural Radiance Field) for 3D consistent renderings from new views.\n",
              "*   **Conclusion:** The technique effectively leverages pre-trained T2I models and synthetic data for controllable, photorealistic material edits in images, demonstrating significant potential despite minor limitations with hidden details."
            ],
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from IPython.display import Markdown\n",
        "\n",
        "Markdown(response.text)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BvaVdm0utZdI"
      },
      "source": [
        "In addition, take a look at how the Gemini model responds when you ask questions about the images within the PDF."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "Dy1x48HmtfWB"
      },
      "outputs": [],
      "source": [
        "response_2 = client.models.generate_content(\n",
        "    model=MODEL_ID,\n",
        "    contents=[file_ref, 'Can you explain the images on the first page of the document?']\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "rmzaDu3ht3xq"
      },
      "outputs": [
        {
          "data": {
            "text/markdown": [
              "The images on the first page of the document serve as the primary visual demonstration of the paper's core contribution: **smoothly editing material properties of objects in images using text prompts.**\n",
              "\n",
              "Here's a breakdown of the four image pairs presented under the \"Input\" and \"Output\" headings:\n",
              "\n",
              "1.  **Teapot Example (Top Left):**\n",
              "    *   **Input:** Shows a smooth, brown teapot.\n",
              "    *   **Prompt:** \"change the roughness of the teapot.\"\n",
              "    *   **Output:** The teapot now appears visibly rougher, with a less reflective, more matte surface, demonstrating the ability to control the \"roughness\" material property.\n",
              "\n",
              "2.  **Cupid Statue Example (Top Right):**\n",
              "    *   **Input:** Shows a solid, stone-like Cupid statue.\n",
              "    *   **Prompt:** \"change the transparency of the cupid statue.\"\n",
              "    *   **Output:** The Cupid statue becomes transparent, revealing a blurred background behind it, showcasing control over the \"transparency\" material property.\n",
              "\n",
              "3.  **Pot Example (Bottom Left):**\n",
              "    *   **Input:** Shows a ceramic or earthenware pot.\n",
              "    *   **Prompt:** \"change the metallic of the pot.\"\n",
              "    *   **Output:** The pot's surface transforms to appear highly metallic and reflective, demonstrating control over the \"metallic\" material property.\n",
              "\n",
              "4.  **Buddha Statue Example (Bottom Right):**\n",
              "    *   **Input:** Shows a golden Buddha statue.\n",
              "    *   **Prompt:** \"change the albedo of the Buddha statue.\"\n",
              "    *   **Output:** The Buddha statue changes its base color (albedo) to a lighter, possibly marble-like or white stone appearance, illustrating the ability to modify the \"albedo\" (base color) material property.\n",
              "\n",
              "In summary, these images vividly illustrate how the proposed method takes an original photograph (Input), interprets a natural language instruction (Prompt) for a specific object within it, and then realistically alters a particular material property of that object (Output) while preserving its overall shape, pose, and the lighting conditions of the scene."
            ],
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "Markdown(response_2.text)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TLlWUv21ucVb"
      },
      "source": [
        "If you observe the area of the header of the article, you can see that the model captures what is happening."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "93U69IwGujsM"
      },
      "source": [
        "## Learning more\n",
        "\n",
        "The File API lets you upload a variety of multimodal MIME types, including images, audio, and video formats. The File API handles inputs that can be used to generate content with `model.generateContent` or `model.streamGenerateContent`.\n",
        "\n",
        "The File API accepts files under 2GB in size and can store up to 20GB of files per project. Files last for 2 days and cannot be downloaded from the API.\n",
        "\n",
        "* Learn more about prompting with [media files](https://ai.google.dev/gemini-api/docs/file-prompting-strategies) in the docs, including the supported formats and maximum length.\n",
        "* Learn more about to extract structured outputs from PDFs in the [Structured outputs on invoices and forms](https://github.com/google-gemini/cookbook/blob/main/examples/Pdf_structured_outputs_on_invoices_and_forms.ipynb) example.\n"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "name": "PDF_Files.ipynb",
      "toc_visible": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}
